Dependency Parsing by Inference over High-recall Dependency Predictions

1. Sander Canisius S.V.M.Canisius@uvt.nl Toine BogersA.M.Bogers@uvt.nl Antal van den Bosch Antal.vdnBosch@uvt.nl Jeroen GeertzenJ.Geertzen@uvt.nl ILK / Language and Information Science Tilburg University Erik Tjong Kim Sang erikt@science.uva.nl Informatics Institute University of Amsterdam Dependency Parsing by Inference over High-recall Dependency Predictions

2. Highlights No modelling, just classification Simultaneously predicting and labelling dependency relations Resolving inconsistencies on the basis of classifier confidence

3. vc obj1 su ik hoor haar zingen Dependent Head Relation ikhoor SU ikhaar - ikzingen -

4. vc obj1 su ik hoor haar zingen Dependent Head Relation hoorik - hoorhaar - hoorzingen -

5. vc obj1 su ik hoor haar zingen Dependent Head Relation haarik - haarhoor OBJ1 haarzingen -

6. vc obj1 su ik hoor haar zingen Dependent Head Relation zingenik - zingenhoor VC zingenhaar -

7. ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor / DET-zingen VC-hoor

8. ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor0.8 / DET-zingen0.5 VC-hoor

9. ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor VC-hoor

10. Features Head features 2-1-2 word & part-of-speech windows Dependent features 2-1-2 word & part-of-speech windows Relative position (LEFT / RIGHT) Distance

11. The relation prediction/classification has a highly skewed class distribution Tends to result in high-precision, low-recall relation predictions Down-sampling the negative class increases recall (At the cost of precision)

13. Language UAS LAS Arabic 74.59 57.64 Bulgarian 82.51 78.74 Chinese 82.86 78.37 Czech 72.88 60.92 Danish 82.93 77.90 Dutch 77.79 74.59 German 80.01 77.56 Japanese 89.67 87.41 Portuguese 85.61 77.42 Slovene 74.02 59.19 Spanish 71.33 68.32 Swedish 85.08 79.15 Turkish 64.19 51.07 Average 78.41 70.80